An estimated 13% of global potential crop yield is lost to diseases each year, with much higher losses occurring under epidemic conditions. For example, northern leaf blight (NLB), also called northern corn leaf blight or turcicum blight, is a fungal foliar disease of maize caused by Setosphaeria turcica (anamorph: Exserohilum turcicum). In the United States and Ontario, NLB has been growing especially severe in recent years, with estimated yield losses rising steadily from 1.9 million metric tons in 2012 to 14 million metric tons in 2015. This estimated yield loss from NLB accounted for one-fourth of all estimated yield losses from disease in 2015, causing an estimated economic loss of $1.9 billion.
To evaluate resistance of plant germplasm and breed for improved resistance, conventional visual assessments of disease incidence or severity are used. However, such assessments are prone to error through inter- and intra-rater variations, which can reduce precision and accuracy of genetic inferences. Similar detection problems arise when attempting to identify or assess the presence of certain pattern characteristics (which may correspond to different abnormal conditions) in other types of specimens.